Several studies have reported the presence of carotid artery calcifications (CACs) on dental panoramic radiographs (DPRs) as a possible sign of arteriosclerotic diseases. However, CACs are not easily visible at the common window level for dental examinations, and dentists, in general, are not looking for CACs. Computerized detection of CACs may help dentists in referring patients with a risk of arteriosclerotic diseases to have a detailed examination at a medical clinic. Downside of our previous method was a relatively large number of false positives (FPs). In this study, we attempted to reduce FPs by including an additional feature and selecting effective features for the classifier. A hundred DPRs including 34 cases with calcifications were included. Initial candidates were detected by thresholding the output of top-hat operation. For each candidate, 10 features and a new feature characterizing the relative position of a CAC with reference to the lower mandible edge were determined. After the rule-based FP reduction, candidates were classified into CACs and FPs by a support vector machine. Based on the leave-one-out cross-validation evaluations, an average number of FPs was 3.1 per image at 90.4% sensitivity using seven features selected. Compared to our previous method, the number of FPs was reduced by 38% at the same sensitivity level. The proposed method has a potential in identifying patients with a risk of arteriosclerosis early via general dental examinations.